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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.
Topic 2	OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.
Topic 3	Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.
Topic 4	Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.
Topic 5	Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?

A. Oracle Open Data
B. Oracle Big Data Service
C. Oracle Cloud Infrastructure (OCI) Data Science
D. Oracle Analytics Cloud

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for free geospatial images.
* Evaluate Options:
* A: Big Data Service-Spark processing, not datasets.
* B: Analytics Cloud-Visualization, not data source.
* C: Data Science-ML platform, not dataset provider.
* D: Open Data-Free public datasets, including geospatial-correct.
* Reasoning: Open Data is OCI's public dataset hub.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to curated datasets, including geospatial images, for public use." A, B, and C serve other purposes-only D delivers free geospatial data.
Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

NEW QUESTION # 48
True or false? Bias is a common problem in data science applications.

A. False
B. True

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

NEW QUESTION # 49
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

A. Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket onto your new notebook session
B. Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
C. Ensure your files and environments are written to the block volume storage under the /home
/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected
D. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re- create all of your file changes

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up notebook without losing work.
* Evaluate Options:
* A: Object Storage-Extra steps, inefficient.
* B: Block volume-Persists data, seamless scale-correct.
* C: Local machine-Risky, cumbersome.
* D: Recreate-Loses work, impractical.
* Reasoning: B uses OCI's built-in persistence.
* Conclusion: B is correct.
OCI documentation states: "Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss." A, C, and D add complexity or risk-only B is optimal per OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

NEW QUESTION # 50
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?

A. Metadata, hyperparameters, metrics only
B. Models and metrics only
C. Model metadata and hyperparameters only
D. Models, model metadata, hyperparameters, metrics

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
* Identify Shareable Artifacts: When sharing, all components-model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)-are accessible.
* Evaluate Options:
* A: Excludes the model itself-incorrect.
* B: Excludes metrics-incorrect.
* C: Excludes metadata and hyperparameters-incorrect.
* D: Includes all components-correct.
* Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").

NEW QUESTION # 51
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?

A. ADSExplainer
B. ADSTuner
C. EvaluationMetrics
D. ADSEvaluator

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the ADS SDK class for visualizing model performance comparison.
* Understand ADS Classes: Each serves a specific ML purpose-visualization requires evaluation tools.
* Evaluate Options:
* A. EvaluationMetrics: Likely a typo-meant EvaluationsMetrics? Not a standalone class for visualization.
* B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)- correct.
* C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
* D. ADSTuner: Tunes hyperparameters, not for visualization.
* Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
* Conclusion: B is correct.
OCI documentation states: "The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines." EvaluationMetrics (A) isn't a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning-only B fits the visualization need per OCI's ADS toolkit.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".

NEW QUESTION # 52
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